65 results about "Physical reality" patented technology

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention discloses a power grid transient process deduction method based on digital twinning, electronic equipment and a medium, and the method comprises the steps: building a digital twinning simulation model based on a new energy single-machine simulation model and a three-dimensional field station virtual model, and outputting a virtual value by the digital twinning simulation model; collecting core parameters in a monitoring period; calculating the deviation ratio of the physical value and the virtual value of the physical station based on the core parameter; and adjusting the transient process control strategy based on an adjustment object and the deviation rate, wherein the adjustment object comprises a virtual value, a core parameter, a scene parameter and a model control mode. The digital twinborn model can be calibrated in real time through deviation ratio closed-loop correction of a physical value and a virtual value, it can be ensured that simulation of the transient process of the power grid is highly consistent with the physical reality, and the precision and reliability guarantee of a deduction result is improved.

The invention relates to the technical field of digital image processing and computer vision, and discloses a remote multi-view video stitching and monitoring method and system based on digital twinning, and the method comprises the steps: constructing a digital twinning model containing geometric, material and semantic information, and calibrating and initially registering a multi-view camera; based on the model and camera parameters, collecting a preprocessed video stream, and refining the camera pose in combination with the model; predicting expected visual content by using the model and the refined pose, and performing non-rigid alignment on the preprocessed video frame; based on the aligned video frames, a model semantic optimization splicing suture line is utilized to generate a reconstructed video, and information loss is repaired in combination with a model context; and finally, performing integrated monitoring interaction on the repaired reconstructed video and the digital twin model, comparing and detecting abnormality, and updating the model by using the reconstructed video. According to the invention, the problems of traditional remote monitoring in the aspects of scene comprehensive perception, intelligent anomaly detection and synchronism of environment digital representation and physical reality are solved.

The invention discloses an intelligent site selection method and system for farmland occupation and compensation balance, and belongs to the technical field of intelligent site selection, and the method comprises the steps: S1, collecting and preprocessing multi-source spatio-temporal data, so as to construct a basic database; s2, constructing a spatial adjacency relation graph, inputting data in the basic database into a graph neural network model, and generating a dynamic evaluation weight vector for a to-be-selected plot; s3, calculating a final utility value of each evaluation index, and calculating a multi-scale ecological balance score of the land parcel by adopting a multiplicative aggregation model in combination with the dynamic evaluation weight vector; s4, predicting the future quality evolution trend of the land parcels by adopting a long-short-term memory network model, and the method can accurately capture and respond to the spatial-temporal dynamic characteristics of the cultivated land ecosystem, so that the evaluation result is more fit with the physical reality.

The technology disclosed relates to Artificial Intelligence techniques for sharing and managing information across organizational boundaries. Specific embodiments of the technology disclosed include the integration of LLM / LMM-based information comparison to enable alignment on a ground truth (or an agreed upon data standard) across multiple documents related to a community's data, and communicating that alignment to the users in the community. As a result, the users can update data and make amendments to documents affecting related actions among multiple community members. in order to resolve any misalignment between data and physical reality. Information exchange can be securable through verifiable credential technology.

The invention discloses a synthetic defect data generation method based on physical feature fusion, and relates to the technical field of defect data generation, and the method comprises the steps: building a real crack feature parameter library of stamping part cracking defects; a parameterized fractal function is adopted to generate a fracture basic form; the Poisson fusion algorithm is used for achieving material sensing fusion of the crack and the stamping part substrate, and edge artifacts are eliminated; performing personalized adjustment on the initial synthetic defect image to generate final synthetic defect data; and using the generated synthetic defect data for deep learning model training. According to the method, a basic system of a real crack characteristic parameter library and CAE stress field data is constructed, a physical real crack form adaptive to a stress field is generated in combination with a parameterized fractal function, edge artifacts are eliminated through Poisson fusion, personalized adjustment is performed through a semi-automatic tool, defect parameters are fed back and optimized by relying on a deep learning model, and thus the crack quality is improved. And finally generating stamping part cracking synthesis defect data with physical authenticity and deep learning adaptability.

The application discloses a shooting simulation training method and system based on trajectory prediction and position feedback, and relates to the technical field of shooting training. The method comprises the following steps: acquiring the trajectory parameters of a bullet, the state information of a firearm, and the position information of a moving target; predicting the position relationship between the bullet and the moving target within a preset time in the future based on the state information of the firearm, the trajectory parameters, and the position information of the moving target; judging whether the hit condition is met according to the position relationship; if the hit condition is met, sending a hit instruction to the signal receiving module of the moving target, and the moving target performing a hit action in response to the hit instruction. The application replaces the virtual rendering scene by introducing the spatial posture, motion information of a real firearm and the trajectory parameters of a bullet for trajectory prediction, so that the trajectory law and the hit determination are closer to the physical reality, the dependence on VR / AR equipment can be reduced, the gap between simulation training and live firing is greatly reduced, and the training effect is effectively improved.

The invention discloses a near-shore disaster-bearing body disaster-causing path tracing method based on a digital-analog experiment, and relates to the technical field of ocean dynamic disaster simulation, the near-shore disaster-bearing body disaster-causing path tracing method comprises the following steps: determining ocean dynamic disaster characteristics of a near-shore research area, integrating multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructing a time-space association database; and based on a three-dimensional ocean and third-generation ocean wave mode, an offshore storm surge-ocean wave coupling numerical mode is established and verified. According to the method, a storm surge-sea wave coupling numerical mode is constructed, multi-disaster-type nonlinear interaction is comprehensively considered, an energy conservation equation is introduced to quantify generation, transmission and dissipation processes of disaster energy, limitation of a traditional single-disaster-type linear model is solved, real-time feedback of a disaster-bearing body three-dimensional dynamic model is combined, and the disaster-bearing body three-dimensional dynamic model is optimized. A closed energy-damage association mechanism is formed, the tracing accuracy of a disaster chain type propagation path is remarkably improved, and it is ensured that recognition of key nodes and dominant paths better conforms to physical reality.

The invention discloses a meta universe interior design system based on generative AI and a dynamic optimization method, and belongs to the technical field of virtual environment design and optimization. The system comprises a data input module, a generative adversarial network model and a dynamic optimization module, and a'design-feedback-iteration 'intelligent closed loop is constructed by fusing user preference parameters, a space structure, material attributes, illumination configuration and behavior data. A GAN model generates a high-fidelity 3D interior design scheme through an encoder, a decoder and a discriminator, and a dynamic optimization module calculates a dynamic adjustment weight based on behavior data such as VR moving line tracking and iteratively updates design parameters in combination with a reinforcement learning reward function and physical constraints. The method comprises the steps of initial scheme generation, real-time feedback collection and multi-round optimization until convergence conditions are met. According to the method, the immersion, the physical authenticity and the user participation degree of the element universe interior design are remarkably improved, and the method has important industrial application value.

The model rendering method provided by the invention comprises the following steps: obtaining a three-dimensional model and a map corresponding to the three-dimensional model, creating a main body material and a shadow material, and carrying out stylization processing on the map to obtain a processed map; wherein the stylized texture comprises applying a drawing texture filter and / or adding a texture effect, distributing the processed chartlet to a corresponding material channel, performing rendering calculation based on the three-dimensional model and the material channel, and outputting a rendered image. According to the method provided by the embodiment of the invention, diversified artistic style rendering effects can be realized by performing stylization processing on the chartlet and creating the special material type, and compared with traditional single physical authenticity rendering, artistry and flexibility of visual performance are greatly improved; and meanwhile, through standardized material distribution and rendering processes, the automation degree and the processing efficiency of rendering operation are remarkably improved.

The invention relates to the technical field of building structure engineering, in particular to a partition wall structure integrated refined modeling and simulation method based on physical reality. A complete and closed technical chain from refined physical modeling to connection and failure linkage algorithm definition and then to whole-process nonlinear simulation to finally realize automatic multi-target parameter optimization is constructed, and'work-damage-failure-influence 'full-life-cycle behaviors of a partition wall subsystem are subjected to full-process nonlinear simulation, so that the whole-life-cycle behavior of the partition wall subsystem is optimized. A rigorous mathematical model and a numerical algorithm are integrated into the performance design of the whole structure, a special design system which is highly innovative, computable and verifiable is formed, and therefore quantitative and performance design of the damping connection structure is completed. By introducing a series of mathematical models, criteria and linkage algorithms with clear physical significance, the black box experience of traditional'cycle reduction coefficient 'is improved.

The invention provides a thoracic surgery teaching assistance method and system, and the method comprises the steps: constructing a thoracic surgery virtual operation interaction environment based on a digital twin technology, and endowing a virtual thoracic cavity tissue model with initial biomechanical parameters; an operation instruction data sequence of an operator for the virtual surgical instrument is collected in real time through the man-machine interaction interface; based on the operation instruction data sequence, calculating an operation disturbance characteristic index representing the motion dispersion degree of an operator in real time by using a sliding time window algorithm; establishing a dynamic evolution mapping model of an operation disturbance characteristic index and a biomechanical parameter, dynamically correcting an initial biomechanical parameter of the virtual tissue according to the index calculated in real time, and when the index exceeds the standard, controlling the parameter to deviate to an easily damaged state so as to simulate histopathologic change caused by rough operation; and performing physical collision response calculation and three-dimensional picture rendering according to the evolved model. According to the invention, dynamic coupling of operation behaviors and organization characteristics is realized, and the physical reality sense of teaching is improved.

The application discloses a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, relates to the technical field of ocean dynamic disaster simulation, determines the ocean dynamic disaster characteristics of a nearshore research area, integrates multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructs a time-space correlation database; based on a three-dimensional ocean and a third-generation sea wave model, a nearshore storm surge-sea wave coupling numerical model is established and verified. Through the construction of the storm surge-sea wave coupling numerical model, the present application comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear models, and combines the real-time feedback of the three-dimensional dynamic model of the disaster-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

The invention provides a hot continuous rolling and finish rolling force prediction method fusing machine learning and a mechanism model, and relates to the technical field of metallurgical process control. By integrating a flow stress mechanism model based on a physical metallurgy rule and a data-driven XGBoost machine learning model, the interpretability of the mechanism model and the fitting ability of machine learning to a complex nonlinear relationship are fully exerted; based on a competitive mechanism between a work hardening effect and a recrystallization / phase change softening effect in a metal deformation process, flow stress mechanism model parameters are optimized by taking a flow stress measured value as a target under the guidance of a PSO optimization algorithm, physical mechanism constraints are embedded, and it is ensured that an optimization result conforms to physical reality; according to the method, key variables of chemical components and process parameters are used as input, a machine learning model for softening rate prediction is established based on an XGBoost algorithm, and a prediction result is used as key parameter input of a mechanism model, so that model precision attenuation caused by working condition change is effectively avoided.

The present application relates to the technical fields of industrial automation, artificial intelligence and computer vision application, in particular to a warehouse automatic access optimization method and system assisted by industrial vision, comprising: collecting original perception data stream, obtaining feature vector of abnormal data area; obtaining physical reality probability and optical false probability through perception classification model processing; calculating visual confidence entropy; in response to the visual confidence entropy being greater than a preset entropy activation threshold, performing illusion evolution deduction to determine a physical confirmation index; executing hierarchical decision logic, including: in response to the visual confidence entropy being not greater than the preset entropy activation threshold, outputting a first access optimization action; in response to the visual confidence entropy being greater than the preset entropy activation threshold, outputting a second access optimization action; obtaining a physical reality label, and correcting the perception classification model and the model used in the illusion evolution deduction; the present application realizes active verification of passive perception failure, and improves decision reliability.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

The invention provides a difficult-to-machine material five-axis milling cutter attitude optimization method and system, and the method comprises the steps: building a three-dimensional thermal coupling finite element model comprising cutter attitude parameters, the cutter attitude parameters comprising a front inclination angle and a side inclination angle of a cutter shaft relative to the surface of a workpiece; based on the three-dimensional thermal coupling finite element model, thermal coupling simulation is conducted on the five-axis milling process under the set cutter posture parameters, and the cutting temperature and cutting force of a cutter-workpiece meshing area are obtained; extracting a cutting temperature peak value and a cutting force peak value from a result of thermal coupling simulation as key output variables; and by taking the front rake angle and the side rake angle as design variables and taking minimization of the key output variable as an optimization target, determining the optimal front rake angle and the optimal side rake angle of the tool by iteratively updating tool attitude parameters and repeating simulation and extraction. The intermittent cutting characteristic of five-axis milling, the complex space tool-workpiece contact relation and the force-heat coupling effect can be accurately simulated, and the simulation result is closer to the physical reality.

The present application relates to the technical field of scene rendering, in particular to a kind of adaptive hybrid rendering method, system and medium based on multidimensional feature, the method is generated by geometry channel G-Buffer containing scene information, comprehensively utilize depth, normal, material and motion and so on Multidimensional feature information;Using decision function, screen space pixel is adaptively divided into visual key area and non-visual key area;For non-visual key area, delay rendering technology is used, for visual key area, path tracking rendering technology is used;The rendering result is weighted and fused to obtain final rendering result.The present application can effectively reduce the invalid calculation overhead of non-visual key area, significantly improve the update efficiency of acceleration structure under dynamic scene, while guaranteeing the physical reality of large-scale dynamic three-dimensional scene, meet the performance requirements of real-time interaction.

The invention relates to a field and water surface survival self-rescue and mutual-rescue VR simulation training system and method, and the method comprises the steps: generating a physical parameter set through constructing a water body-vegetation-human body coupling control equation set, and carrying out the dynamic boundary mapping based on student movement data and equipment signals, and generating a dynamic boundary condition and a rescue tool constraint; calling physical parameters to realize multi-physical field coupling solution, and generating a physical field output set in combination with collision response; converting the physical field data into a tactile feedback waveform and a rendering instruction set by utilizing a vegetation stiffness matrix, and realizing cross-modal synchronous feedback of mechanical characteristics and visual special effects; and dynamically optimizing the physical parameter set based on training feedback data to form a training difficulty adaptive closed-loop system. According to the method, the problem of insufficient training effect migration caused by multi-physical field coupling distortion and environment response stiffness in the prior art is solved, and the physical authenticity of a virtual training scene and the adaptability of student actions are remarkably improved.

The invention relates to a method, a device and a system for preventing fingertip pinching mold penetration in VR gesture tracking and application. The method comprises the following steps: acquiring real-time three-dimensional space coordinates of a thumb fingertip joint point and an index finger fingertip joint point, and calculating a distance d between the thumb fingertip joint point and the index finger fingertip joint point; establishing a multi-level threshold group including an approach threshold and a contact threshold, establishing an anti-mold-penetrating preparation mode and an anti-mold-penetrating constraint mode, and triggering a corresponding mode based on the relationship between d and the multi-level threshold group; after the anti-mold-penetration preparation mode is triggered, the three-dimensional space coordinate positions of the thumb fingertip joint point and the index finger fingertip joint point are corrected; driving a virtual hand model to render based on the corrected fingertip position and other uncorrected hand joint point data; the device and the system are implemented based on the method, and are applied to the correction of kneading mold penetration in a VR man-machine gesture interaction process. According to the method, the interaction naturalness and immersion are remarkably improved, stable system performance is ensured, the problem of physical authenticity of micro-interaction is solved, and coherence, smoothness and naturalness of the whole hand posture are ensured.

The application provides an optical remote sensing physical adversarial sample generation method based on a multi-task loss function optimization, and the method comprises the following steps: step one, data and model preparation; step two, arranging a parameterized physical adversarial sample in a training set image; step three, training the parameterized physical adversarial sample; step four, arranging the optimized parameterized physical adversarial sample in a test set image, and testing the adversarial performance by using a test model; and output: a physical adversarial sample design drawing; a series of physical adversarial samples are prepared according to the proposed generation method, and experiments are conducted in a data space and a physical real world to test the interference ability of the physical adversarial samples; the experimental results fully prove the effectiveness of the proposed generation method, and the generation method has wide application value.

The invention provides a plateau photovoltaic converter monitoring method and system based on physical information fusion, and the method comprises the steps: carrying out the modal feature extraction, interaction, multi-time-sequence feature extraction and channel compression of multi-dimensional key data, and mining the data value; the physical constraint fusion layer fuses physical constraints such as power conservation, thermodynamic cycle and insulation degradation into model training to ensure that modeling accords with physical reality and improve monitoring credibility and accuracy; the high and cold adaptive decoder calculates a health index based on accumulated damage and a thermal resistance change rate, early warns potential faults, and optimizes the performance of the converter to adapt to a plateau environment; a label-free training mechanism improves the problem of scarcity of labeled data, and the generalization and robustness of the model are improved; the lightweight model design and the efficient inference engine are integrated, and efficient and stable operation of the system on an embedded platform is guaranteed. According to the invention, the monitoring precision and the fault early warning timeliness are significantly improved, and a comprehensive, efficient and intelligent solution is provided for reliability monitoring of the plateau photovoltaic converter.

An approach for determining the uncertainty of sensor signals synthesized by a generative machine learning system (61) u A computer-implemented method (900) for uncertainty relating to the probability of observing sensor signals in physical reality, the method comprising the steps of: obtaining (901) a noise sample (z); performing (902) Bayesian inference on the generative machine learning system (61) using the noise sample (z) as input to determine a posterior prediction distribution of a sensor signal that would otherwise be synthesized by the generative machine learning system (61) using the noise sample (z) as input to the generative machine learning system (61); and providing (903) a measure of the variability of the posterior prediction distribution as uncertainty about the sensor signal synthesized from the noise sample (z), wherein the method is characterized in that the posterior prediction distribution characterizes the distribution of potential features of the sensor signal that would otherwise be generated using the noise sample (z) as input.

The application discloses a MEMS damage mode rapid analysis method and system, and aims to solve the problems that a traditional MEMS mode analysis method is difficult to truly reflect the influence of service damage, or the calculation cost of full model damage analysis is too high. The method comprises the following steps: performing high-fidelity damage evolution simulation on a MEMS structure; performing CB mode reduction of a damage beam and constructing a transfer matrix; performing multi-body system discretization and constructing a MEMS system transfer equation; and solving the vibration characteristics of the MEMS system. Through a series of mutually supporting technical steps, the application systematically solves the core contradiction existing in the MEMS damage mode analysis in the prior art, and brings substantial progress and beneficial effects in the aspects of physical reality, calculation efficiency, method universality and engineering practicability.

This specification provides a method and apparatus for multimodal data alignment and enhancement in industrial large-scale model training. By integrating an industrial knowledge graph with physical law constraints into a cross-modal semantic distillation architecture, heterogeneous features such as vibration, vision, and process parameters are aligned in a semantic space that conforms to engineering laws, effectively solving the problem of fragmented semantic expression and enhancing the depth of understanding of industrial processes. By introducing a dual-discriminator adversarial enhancement strategy that includes a realism discriminator and a physical consistency discriminator, and combining the constraints of an industrial anomaly pattern library, it ensures that the generated data satisfies both statistical distribution characteristics and physical reality, significantly reducing the data distortion rate. This provides high-fidelity training data for industrial large-scale models, ultimately achieving a comprehensive improvement in model convergence speed, anomaly detection accuracy, and decision credibility, meeting the application requirements of high precision and high real-time performance in industrial scenarios.

This invention discloses a dynamic co-simulation method for satellite electromagnetic docking processes, belonging to the field of spacecraft electromagnetic docking dynamics simulation. The simulation method includes the following steps: First, a 3D model of the spacecraft containing electromagnets is performed in Ansys Maxwell, and a spacecraft dynamics simulation model is established in Syslab. Then, simulation boundary conditions and initial conditions are set. Co-simulation is performed to establish a closed-loop dynamic information transmission process for electromagnetic and dynamic interaction. Subsequently, based on the simulation information from the previous step, the motion process of the current step is analyzed step by step in Syslab. At this time, the external control system is allowed to detect and control the electromagnet current. Finally, the co-simulation process is repeated, dynamically updating the spacecraft's motion state until it stops, realizing the electromagnetic docking dynamics simulation of electromagnetic field and motion coupling. This invention can overcome the shortcomings of traditional electromagnetic simulation in solving the dynamics simulation of electromagnetic field and motion coupling, and is more in line with the physical reality of spacecraft on-orbit docking.

The application discloses a kind of elbow fluid-structure acoustic coupling calculation and noise reduction method, belong to pipeline test technical field.The application constructs the three-dimensional physical model of elbow and establishes elbow fluid-structure coupling model, accurately reflects the dynamic correlation between the internal fluid flow characteristics of elbow and solid structure response, by solving fluid velocity field, pressure field and turbulent flow characteristics, can capture the flow field disturbance caused by inertia, centrifugal force and elbow geometric change in the process of flow, quantitatively describes the dynamic load variation law that fluid exerts on pipe wall, effectively improves the understanding of complex elbow flow field behavior, extracts wall surface pressure distribution data and generates fluid disturbance data, can accurately predict the noise source distribution characteristics generated by flow-induced vibration, overall, it breaks through the problem that traditional single-field modeling method is insufficient to describe actual working condition, greatly improves the physical reality and prediction accuracy of simulation, effectively avoids the high cost and low efficiency problem caused by traditional trial-and-error method.

The invention relates to the technical field of man-machine interaction, and discloses a virtual musical instrument control device based on gesture recognition, and the device comprises a magnetic field sensing module which is used for detecting the magnetic field disturbance of multiple targets; the multi-point state sensing module is used for resolving independent and associated state vectors of each target; the dynamic physical model core module is used for dynamically modulating physical model parameters on the basis of the associated state vector, and solving on the basis of taking an independent state vector as excitation input to obtain a vibration state result; and a coupled multi-modal rendering module for synchronously generating audio and differentiated haptic feedback signals based on the result. The method correspondingly realizes the above functions. According to the method, the dynamic coupling relationship between the multi-point coordination action of the user and the physical attribute of the virtual musical instrument is established, and the sound tactile feedback of a unified physical cause is provided, so that the physical reality, the continuous expressive force and the immersion of virtual musical instrument interaction are remarkably improved.

The present invention is directed to a method for autonomously controlling a device, whereby the device moves generally in the physical real world and no longer needs to be further restricted with respect to its physical configuration. The method yields the advantage that the device autonomously learns and continuously improves its learned knowledge or behavior. Generally, the method overcomes the drawback of the prior art that training data must first be created, as in traditional artificial intelligence methods. Generally, the method can be used universally, and the device automatically learns and constantly modifies its own knowledge base. Furthermore, devices set up to perform the method are proposed, as well as system configurations comprising some of the proposed devices. Furthermore, computer program products and computer-readable storage media are proposed for performing the method steps or for causing a computer to perform the method.

The application relates to the technical field of computer hardware, and particularly discloses an intelligent control system of a DDR6 memory structure of a desktop computer mainboard on-board BGA central processor, which comprises a hardware perception layer, a behavior modeling layer, a collaborative decision-making layer and a dynamic execution layer, realizes intelligent collaborative control of the processor and the memory subsystem from physical electrical characteristics to logical access behavior through real-time perception of hardware states, construction of a digital twin model, collaborative optimization of prefetching and scheduling strategies and closed-loop execution feedback, and thereby improves memory access efficiency, reduces delay and enhances system stability. The system not only focuses on the access mode at the software level, but also quantifies physical layer constraints such as signal integrity and power supply noise into computable model parameters, so that subsequent optimization decisions are established on the basis of the digital twin reflecting the real hardware state, and the problem that traditional control logic is disconnected with the physical reality is overcome, thereby laying a data foundation for the unification of high performance and high stability.